MISIM: An End-to-End Neural Code Similarity System

By Fangke Ye, Sheng-Tian Zhou, Anand Venkat, Ryan Marucs, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, T. Mattson, Tim Kraska, Pradeep K. Dubey and 2 more. 2020

24 citations recorded by the source corpus.

Subfields: Natural Language Processing, Code Generation & Program Synthesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Nan Duan
2. Neel Sundaresan
3. Alexey Svyatkovskiy
4. Shao Kun Deng
5. Michele Tufano
6. Colin B. Clement
7. Shujie Liu
8. Daya Guo

In Code Generation & Program Synthesis

1. Ge Li
2. Nan Duan
3. Daya Guo
4. Neel Sundaresan
5. Shao Kun Deng
6. Colin B. Clement
7. Shengyu Fu
8. Michele Tufano

Citations in the corpus

• CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021, methodology): “MISIM-GNN [86] learns a structural representation of code from context-aware semantic structure designed specifically to lift semantic meaning from the code syntax.”